2.6 Statistical analysis
For each habitat type and season, we decided the cumulative species
richness and species abundance across all samplings and assembled the
community matrix. Pollinator’s richness and abundance were compared
between different habitats and seasons, using linear mixed–effect
models with habitats and seasons as predictor variables and pollinators
as response variables. The statistical analysis was performed in R,
version 4.0.3. Tukey’s test was carried out to find significance.
PAST. Paleontological Statistics (Hammer et al., 2001) Version 3.17
computed the diversity indices. Random matrices with two samples are
generated, each with the same row and column totals as in the original
data matrix which provided the significance of diversity between groups.
Pollinator’s community compositions of different habitats (FT, GL, OT,
MH) were analyzed by Non–metric Multidimensional Scaling (NMDS) of the
abundance data employing the function meta MDS which is incorporated in
the statistical package Vegan (Oksanen et al., 2013) and NMDS result
with sample plots of different abundance scores was fitted with
different habitats using the package ’ggplot2’ (Wickham, 2016).
NMDS was followed by statistical analyses: Adonis (Permutational
Multivariate Analysis of variance), ANOSIM (Analysis of similarities),
and SIMPER (Similarity Percentage Analysis).
Adonis was carried out following NMDS to analyze statistically if the
pollinator community differs between the habitats. It provides the
p–value to determine the statistical significance. ANOSIM, on the other
hand, was used to determine if the differences of pollinator’s community
between the habitats are significant. In addition to the significant
difference tests, Simper analyses were used to identify those species
that contributed most to the observed pollinator’s community differences
(Clarke & Gorley, 2001).
To find relations between the environmental variables and the species
composition, ordinations were performed on insect pollinators. For the
pollinator community, a detrended correspondence analysis (DCA) was
carried out to decide whether unimodal or linear ordination methods were
appropriate (Lepˇs & ˇSmilauer, 2003). Based on this data, a redundancy
analysis (RDA) was carried. Environmental variables were backward
selected (p < 0.05) using the ‘ANOVA’. A Monte Carlo
permutation test with 999 iterations was used to assess the significance
of the ordination.
NMDS, RDA, and all of the three procedures (Adonis, ANOSIM, and SIMPER)
were carried out in software R 4.0.3 using the ”vegan” package (Oksanen
et al., 2013).
Venn diagrams showing the species sharing between the habitats were
performed in R by using the “Venn Diagram” package employing the
function draw. quad. venn.